Anna Vartapetiance

“Our Little Secret”: pinpointing potential predators

The word “Paedophilia” has come a long way from its Greek origin of child-companionship to a Mental Disorder, Social Taboo and Criminal Offence. Various laws are in place to help control such behaviour, protect the vulnerable and restrain related criminal offences. However, enforcement of such laws has become a significant challenge with the advent of social media creating a new platform for this old crime. This move necessitates consideration of approaches that are suited to this new platform and the way in which it affects the Cycle of Entrapment. This paper reviews definitions of, and features of, paedophilia and other related –philias, and sexual offences against children, and seeks through the understanding of these to determine where specific detection approaches are effective. To this end, we present our own detection approach which is geared towards predatory behaviours, which can be a precursor to sexual offences against children, and which directly references this Cycle of Entrapment. Our approach has shown early promise with an F1 score of 0.66 for training data but only achieving 0.48 for testing data on a collection of chat logs of sexual predators. The results were later improved to achieve an F1 score of 0.77 for train and 0.54 for test data based on the approach.

Deception detection for the tangled web

Deception is a reasonably common part of daily life that society sometimes demonstrates a degree of acceptance of, and occasionally people are very willing to be deceived. But can a computer identify deception and distinguish it from that which is not deceptive? We explore deception in various guises, differentiating it from lies, and highlighting the influence of medium and message in both deception and its detection. Our investigations to date have uncovered disagreements relating to the measurements of such cues, and variations in interpretations, as could be problematic in building a deception detection system.

Deception detection: dependable or defective?

Abstract How do human beings tell the difference between truths and lies, and avoid being deceived? And is it possible for a machine to determine the veracity of any given statement or set of statements prior to incorporating such statements in a knowledge base, or to determine whether the deception even exists at the statement level? This paper reviews past research in deception and its detection to explore such questions. We focus in on various inconsistencies, contradictions, and other difficulties in recent deception research, and show how the nature of the deception largely dictates the methods that can be deployed effectively in detection by reference to several experiments on materials which can have a strongly deceptive framing.